Medical imaging systems are distinguished in that internal structures of an examination object or patient can be investigated without having to carry out surgical interventions thereon.
Examples of such imaging systems or imaging equipment are ultrasonic systems, X-ray systems, X-ray computed tomography (CT) systems, positron emission tomography (PET) systems, single photon emission tomography (SPECT) systems or magnetic resonance (MR) systems.
In particular, X-ray image recording devices enable tomographic imaging wherein a number of projections of the object under investigation are recorded from different angles. From these projections, a two-dimensional sectional image or a three-dimensional volume image of the examination object can be computed.
An example of such a tomographic imaging system is the aforementioned X-ray CT. Methods for scanning an examination object with a CT system are commonly known. Herein, for example, circular scans, sequential circular scans with table advance or spiral scans are used. Other types of scanning, which do not involve circular movements are also possible, such as scans with linear segments. With the aid of at least one X-ray source and at least one detector arranged opposite thereto, X-ray attenuation data of the examination object are recorded from different recording angles and these attenuation data or projections thus gathered are used for computation via appropriate reconstruction methods to produce sectional images or three-dimensional images through the examination object.
Due to their non-invasive functioning, medical imaging devices nowadays play a significant part in the examination of patients. The representations of the internal organs and structures of a patient generated by the imaging systems are used for widely differing purposes, for example, preventative examinations (screening), for tissue sample taking (biopsy), for the diagnosis of the causes of illness, for planning and carrying out operations or for preparing therapeutic measures. In the field of radiation therapy, for example, radiological data are needed in order to plan the irradiation with regard to the distribution of the dose. Herein, the dose in the region to be treated must be above a threshold and in the remaining tissue, in particular in sensitive organs, it should be as low as possible in order to prevent secondary damage.
For this purpose and for many other of the above-mentioned uses or tasks, the segmentation of particular target structures is useful and even required. Such target structures can be, for example, defined bone structures, particular organs, vessel structures or defects or lesions, for example, tumors which must first be identified and have possibly to be extracted from the image data.
Segmentation should be understood, in general, to mean the generation of regions of coherent content by grouping together adjacent image points according to a particular criterion. This criterion can be, for example, the belonging to a particular structure. The image data belonging to the structure can then, for example, be marked and/or virtually separated from the remaining image data and be considered separately or made available for further analyses.
A reliable and sufficiently accurate spatial separation of the segmentation of structures is essential for many uses.
Organs or lesions are identified by manual contouring or by automatic segmentation. Manual contouring, wherein an operator draws in border lines or border points while observing the image data on a screen with the aid of a graphical user interface, on the basis of which the segmentation then occurs, is more reliable with regard to the accuracy of the association of image points to structures and therefore applies, as before, as a reference. However, the time expenditure required for this is enormous since the markings have to be set by the user slice by slice.
Automatic segmentation algorithms enable, in principle, the time and personnel cost to be reduced and simultaneously the objectivity of the segmentation to be increased. Alongside primitive algorithms with linear edge detectors (Sobel-Scharr operator), enhanced algorithms take account of the statistical significance of contours, i.e. the segmentation weights the linear edge response with the background noise. However, the resolution and statistical properties in tomographic image data sets are non-trivial, i.e. the noise is non-stationary and anisotropic and the resolution is dependent on the position in the scanning field and the direction. Therefore, this consideration on the basis of the image data without additional information regarding the data acquisition succeeds only to a limited extent. Results of the automatic segmentation are therefore routinely corrected manually.
By contrast therewith, it is an object of the present invention to provide improved image data reconstructed from tomographic scan data which are suitable for a stable automatic segmentation via linear edge detection.